scholarly journals Polynomial mapping functions

2021 ◽  
pp. 307-310
Keyword(s):  
Polynomial Mapping ◽  
Mapping Functions

Study of Polynomial Mapping Functions in Video-Oculography Eye Trackers

2012 ◽  
Vol 19 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Juan J. Cerrolaza ◽  
Arantxa Villanueva ◽  
Rafael Cabeza
Keyword(s):  
Polynomial Mapping ◽  
Mapping Functions ◽  
Video Oculography

Assessment of troposphere mapping functions using three-dimensional ray-tracing

GPS Solutions ◽  
2013 ◽  
Vol 18 (3) ◽  
pp. 345-354 ◽  
Author(s):  
Landon Urquhart ◽  
Felipe G. Nievinski ◽  
Marcelo C. Santos
Keyword(s):  
Ray Tracing ◽  
Three Dimensional ◽  
Mapping Functions

Systematic errors of mapping functions which are based on the VMF1 concept

GPS Solutions ◽  
2014 ◽  
Vol 19 (2) ◽  
pp. 277-286 ◽  
Author(s):  
Florian Zus ◽  
Galina Dick ◽  
Jan Dousa ◽  
Jens Wickert
Keyword(s):  
Systematic Errors ◽  
Mapping Functions

Mapping functions and data redistribution for parallel files

2008 ◽  
Vol 46 (3) ◽  
pp. 213-236 ◽  
Author(s):  
Florin Isaila ◽  
Walter F. Tichy
Keyword(s):  
Mapping Functions ◽  
Data Redistribution

scholarly journals Fully Convolutional Network for Consistent Voxel-Wise Correspondence

2020 ◽  
Vol 34 (07) ◽  
pp. 12935-12942 ◽  
Author(s):  
Yungeng Zhang ◽  
Yuru Pei ◽  
Yuke Guo ◽  
Gengyu Ma ◽  
Tianmin Xu ◽  
...  
Keyword(s):  
Vector Fields ◽  
State Of The Art ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Cone Beam ◽  
Nonlinear Mapping ◽  
Convolutional Network ◽  
Mapping Functions ◽  
Fully Convolutional Network

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

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